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Uncertainties of climate change impacts in agriculture. Senthold Asseng. F. Ewert, P. Martre, R.P. Rötter, D.B. Lobell, D. Cammarano, B.A. Kimball, M.J. Ottman, G.W. Wall , J.W. White , M.P. Reynolds, P.D. Alderman, P.V.V. Prasad, P.K. Aggarwal, J. Anothai, B. Basso,
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Uncertainties of climate change impacts in agriculture Senthold Asseng F. Ewert, P. Martre, R.P. Rötter, D.B. Lobell, D. Cammarano, B.A. Kimball, M.J. Ottman, G.W. Wall, J.W. White, M.P. Reynolds, P.D. Alderman, P.V.V. Prasad, P.K. Aggarwal, J. Anothai, B. Basso, C. Biernath, A.J. Challinor, G. De Sanctis, J. Doltra, E. Fereres, M. Garcia-Vila, S. Gayler, G. Hoogenboom, L.A. Hunt, R.C. Izaurralde, M. Jabloun, C.D. Jones, K.C. Kersebaum, A.-K. Koehler, C. Müller, S. Naresh Kumar, C. Nendel, G. O’Leary, J. E. Olesen, T. Palosuo, E. Priesack, E. EyshiRezaei, A.C. Ruane, M.A. Semenov, I. Shcherbak, C. Stöckle, P. Stratonovitch, T. Streck, I. Supit, F. Tao, P. Thorburn, K. Waha, E. Wang, D. Wallach, J. Wolf, Z. Zhao and Y. Zhu
Overview • AgMIP • Crop models – modeling CO2 • Model uncertainty • What is it? • Quantification • Comparison with other sources • Can it be reduced? • Conclusions
AgMIP Agricultural Model Intercomparison and Improvement Project Led by Cynthia Rosenzweig, James W. Jones, Jerry Hatfield & John Antle • Goals • To improve the characterization of risk of hunger and world food security due to climate change, • To enhance adaptation capacity in both developing and developed countries. www.agmip.org
AgMIP Wheat AgMIP 30 wheat models • AgMIP : • combines climate – crop – economic models in a multi-model approach • started in 2010, open, > 600 members from around the world, >30 projects AgMIP Wheat Rosenzweig et al. 2013 AFM
Wheat yield and climate Temperature H2O Light CO2 Management Genotype Carter 2013 Soil
Scale Crop models E x M P G
APSIM - NWheat C Nwheat SoilN SoilWAT
Models vs observations Model input output observation outputobservation 1. wrong input 2. wrong/poor estimate for input 3. wrong observation 4. wrong model/routine a) wrong number b) wrong unit c) value with large variability d) outside model design c) ‘not a measurement’ - just another ‘model’
Photosynthesis leaf cell CO2 H2O CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O
Simple approaches to compute Photosynthesis: RUE - model RUE Sinclair and Weiss (2010) In: Principles of Ecology in Plant Production Monteith1977 PTRSL
Climate change - Photosynthesis leaf CO2 H2O CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O • Radiation use effciency (RUE) and transpiration effciency (TE) • both increases with increased CO2
RUE model RUE = Radiation use efficiency in g[crop] MJ-1[intercepted light] dW/dt = RUE xFCO2x I0x [1-exp(-k . LAI)] incoming light interception FCO2 Reyanga et al. 1999 EMS
Observed grain yield – CO2 effect Grain Yield (t/ha) +CO2 = 550ppm (by 2050) Observed data after Kimball et al. 1995 GCB dry dry+CO2 wet+CO2 wet
Observed grain yield – CO2 effect Grain Yield (t/ha) +CO2 = 550ppm (by 2050) Observed data after Kimball et al. 1995 GCB low N low N+CO2
Observed & simulated grain yield – CO2 effect Grain Yield (t/ha) observed & simulated Asseng et al. 2004 FCR dry dry+CO2 wet+CO2 wet low N low N+CO2
Modeling climate impact Climate models • Climate models (+scenarios) • e.g. Crop model • (or model for: • - hydrology, • biodiversity, • health…) • e.g. Economic model (or model for: • land-use…) Climate models Impact model Impact model Impact model
A meta-analysis of crop yields (wheat) Challinor et al. 2014 Nature CC
Distinguishing variability from uncertainty Due to model, process, measurements errors Variability = Natural variability in space & time e.g. impact simulation Lehmann & Rillig 2014 Nature CC
Distinguishing variability from uncertainty Natural variability Uncertainty After Lehmann & Rillig 2014 Nature CC
AgMIP Wheat - Background Crop model = main tool to assess climate change impact But, simulated effect due to chosen crop model ?
AgMIP Wheat Pilot • 27 wheat models • 4 contrasting field experiments (natural variability) • Standardized protocols • “Blind test” • Full calibration • Sensitivity analysis
AgMIP Wheat Pilot 27 wheat models 4 contrasting field experiments Wheat area after Monfreda et al. (2008) CIMMYT’s mega-environments (ME) for wheat ME 11, High rainfall; cold temperature, winter wheat ME 2, High rainfall; temperate temperature, spring wheat ME 1, Irrigated; temperate temperature, spring wheat ME 4, Low rainfall; temperate temperature, spring wheat
Observations versus simulations Line = median Box = 50% Bars = 80% Asseng et al. 2014 Nature CC
Observations versus simulations Fully calibrated “Blind” 13.5% = coefficient of variation for field experimental observation (Taylor et al. 1999) Asseng et al. 2014 Nature CC
Observations versus simulations “Blind” Asseng et al. 2014 Nature CC Fully calibrated
Model detail Relative RMSE (%) Challinor et al. 2014 Nature CC
Model response to changes in T, rainfall and CO2 to climate change scenario (A2 2100) “Blind” fully calibrated 50% of models with the closest simulations to the observed yields across all location 50% of models with closest simulation per location • e.g. the best models (i.e. smallest RMSE with observations) have smallest CV at 3 locations, but not at AU; • i.e. performance of models with historical data is no guidance for future impact studies Asseng et al. 2014 Nature CC
Model response to rainfall Argentina Australia Line = median Box = 50% Bars = 80% Asseng et al. 2014 Nature CC
Model response to CO2 and T Argentina Australia CO2 response: Amthor 2001, Ewert et al. 2002, Hogy et al. 2010, Kimball 2011, Ko et al., 2010, Li et al. 2007 T response (extrapolated): Amthor 2001, Singh et al. 2008, Xiao et al. 2005 Simulated % yield change Line = median Box = 50% Bars = 80% Asseng et al. 2013 Nature CC
Model response to heat stress 7 x 35 oC after anthesis Line = median Box = 50% Bars = 80% Models with heat stress routine Asseng et al. 2013 Nature CC
Maize model response 23 models Bassu et al. 2014 GCB
Modelling climate impact Climate models • Climate models (+scenarios) • e.g. Crop model • (or model for: • - hydrology, • biodiversity, • health…) Impact model
Impact uncertainties A2 scenario for Mid-Century Model uncertainty in simulating climate change yield impact Mean exp CV% (Taylor et al. 1999) Uncertainty due to 16 GCM’s scenarios Asseng et al. 2014 Nature CC
Multi-model ensembles to reduce uncertainty 13.5% = Mean exp CV% (Taylor et al. 1999) Asseng et al. 2014 Nature CC
Multi-model ensembles to reduce uncertainty Colors represent different CO2 levels Mean (+/- STD) of all locations Asseng et al. 2014 Nature CC (13.5% = Mean exp CV% (Taylor et al. 1999))
Reducing uncertainty via model improvements
Model improvements to reduce uncertainty PD Alderman, E Quilligan, S Asseng, F Ewert and MP Reynolds (Editors) CIMMYT, El Batan, Texcoco, Mexico June 1921, 2013 Bruce Kimball Wall et al. 2011 GCB; Ottman et al. 2012 AJ • Improve high temperature impacts in models
Conclusions • Many of the crop models can reproduce observed experiments • However, there is an uncertainty in climate change impact assessments due to crop models • This uncertainty is similar to experimental error, but larger than from GCM’s • Uncertainty in modeling T and T x CO2 interactions • >>> model improvements • Multi-model ensembles can reduce simulated impact uncertainties. Contact: Senthold Asseng, sasseng@ufl.edu